Introduction to CUDA Kernel Programming

Master 2D thread indexing in CUDA by mapping multidimensional grids to linear memory. You will implement row-major indexing, build a naive matrix multiplication kernel, and apply 2D grid-stride loops. These skills allow you to process large, rectangular datasets efficiently while ensuring your kernels remain scalable and flexible across various GPU architectures.

In this course, you will tackle global memory latency by harnessing the power of fast, on-chip shared memory. You will learn to synchronize threads using shared memory, implement a boundary-safe tiled matrix multiplication algorithm, and empirically compare it against a naive implementation using validated benchmarks.

Apply your CUDA skills to image buffers by processing pixels on the GPU. You will map RGB data to threads, handle boundary conditions, and build a pipeline chaining grayscale conversion and Sobel edge detection. Finally, you will validate and export results, mastering real-world GPU image manipulation.